Using machine learning to predict fractures in postmenopausal women.
Predicting who will fracture: Exploration of machine learning in the observational Women's Health Initiative Study dataset.
This study is looking at how smart computer programs can help predict the risk of fractures in younger postmenopausal women aged 50-64, using information from a large health study, to find better ways to identify those who might need early help with osteoporosis.
Quick facts
| Grant type | R21 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of California Los Angeles NIH-funded |
| Lab location | 1 site (Los Angeles, United States) |
| Project ID | NIH-10707881 on NIH RePORTER |
What this research studies
This research investigates how machine learning can enhance the prediction of fracture risk in younger postmenopausal women aged 50-64 by analyzing data from the Women's Health Initiative Study. The study aims to develop and validate various machine learning models to better assess osteoporosis risk and identify individuals who may benefit from early intervention. By utilizing a large dataset of over 160,000 participants, the research seeks to improve existing risk assessment tools that currently do not effectively differentiate between women at risk and those who are not. This approach could lead to more accurate screening and preventive measures for osteoporosis-related fractures.
Who could benefit from this research
Good fit: Ideal candidates for this research are postmenopausal women aged 50-64 who may be at risk for osteoporosis and related fractures.
Not a fit: Patients who are not postmenopausal or those over the age of 64 may not receive direct benefits from this research.
Why it matters
Potential benefit: If successful, this research could lead to improved screening methods that help prevent fractures in at-risk postmenopausal women.
How similar studies have performed: Previous research using machine learning for osteoporosis risk assessment has shown promise, but this study aims to apply these methods specifically to an American population and a larger dataset.
Where this research is happening
Los Angeles, United States
- University of California Los Angeles — Los Angeles, United States (Active)
Researchers
- Principal investigator: Crandall, Carolyn Janet — University of California Los Angeles
- Study coordinator: Crandall, Carolyn Janet
About this research
- This is an active NIH-funded research project — typically early-stage science, not a clinical trial accepting patient enrollment.
- Some NIH-funded labs run parallel clinical studies or seek volunteers for related work. To check, contact the principal investigator or institution listed above.
- For full project details, budget, and progress reports, visit the official NIH RePORTER page below.